The anomaly detection aims to identify samples, which in some form deviates from the majority. I It is an optional part of the data-processing, which can improve the standard algorithms, or it is used at its own. An examples of applications are identification of measurement errors (faulty sensors), detection of faulty behavior of some industry process (anomaly might indicate a malfunction), fraud detection in credit card transactions, monitoring of health or environmental processes, etc. Most approaches to the anomaly detection are very computationally expensive, which makes their practical applicability questionable, especially in tasks requiring quick responses and constant update. Our research targets to make both parts (detection and update) real-time.Behavior modeling is a specific application of anomaly detection, where we search for behavior of individuals significantly different from the crowd. The applications are typically in security, e.g. terrorist attack prevention.

Contact person: Tomas Pevny

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